Academic Journal
Multi-view graph clustering via dual attention fusion and collaborative optimization.
| Τίτλος: | Multi-view graph clustering via dual attention fusion and collaborative optimization. |
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| Συγγραφείς: | Wang Z; School of Information Engineering, Yancheng Institute of Technology, No.1 Hope Avenue Middle Road, Yancheng, 224051, Jiangsu, China. Electronic address: wangzuoweiedu@outlook.com., Xu S; School of Information Engineering, Yancheng Institute of Technology, No.1 Hope Avenue Middle Road, Yancheng, 224051, Jiangsu, China; College of Information and Communication Engineering, Harbin Engineering University, No.145 Nantong Street, Harbin, 150001, Heilongjiang, China; Jiangsu Provincial Engineering Technology Center for Multimodal Perception and Intelligent Control of Offshore Wind Power Systems, Yancheng Institute of Technology, No.1 Hope Avenue Middle Road, Yancheng, 224051, Jiangsu, China. Electronic address: xusen@ycit.cn., Guo N; School of Information Engineering, Yancheng Institute of Technology, No.1 Hope Avenue Middle Road, Yancheng, 224051, Jiangsu, China; Jiangsu Provincial Engineering Technology Center for Multimodal Perception and Intelligent Control of Offshore Wind Power Systems, Yancheng Institute of Technology, No.1 Hope Avenue Middle Road, Yancheng, 224051, Jiangsu, China; Key Laboratory of Computer Network and Information Integration, Southeast University, No.2 Southeast University Road, Nanjing, 211189, Jiangsu, China. Electronic address: guonaixuan@ycit.edu.cn., Bian X; School of Information Engineering, Yancheng Institute of Technology, No.1 Hope Avenue Middle Road, Yancheng, 224051, Jiangsu, China; Jiangsu Provincial Engineering Technology Center for Multimodal Perception and Intelligent Control of Offshore Wind Power Systems, Yancheng Institute of Technology, No.1 Hope Avenue Middle Road, Yancheng, 224051, Jiangsu, China. Electronic address: xsbian@ycit.edu.cn., Xu X; School of Information Engineering, Yancheng Institute of Technology, No.1 Hope Avenue Middle Road, Yancheng, 224051, Jiangsu, China; Jiangsu Provincial Engineering Technology Center for Multimodal Perception and Intelligent Control of Offshore Wind Power Systems, Yancheng Institute of Technology, No.1 Hope Avenue Middle Road, Yancheng, 224051, Jiangsu, China. Electronic address: xxf@ycit.cn., Yao S; School of Information Engineering, Yancheng Institute of Technology, No.1 Hope Avenue Middle Road, Yancheng, 224051, Jiangsu, China; Jiangsu Provincial Engineering Technology Center for Multimodal Perception and Intelligent Control of Offshore Wind Power Systems, Yancheng Institute of Technology, No.1 Hope Avenue Middle Road, Yancheng, 224051, Jiangsu, China. Electronic address: shanliang.yao@ycit.edu.cn., Ben X; College of Information and Communication Engineering, Harbin Engineering University, No.145 Nantong Street, Harbin, 150001, Heilongjiang, China; School of Information Science and Engineering, Shandong University, No. 72 Binhai Road, Qingdao, 266237, Shandong, China. Electronic address: benxianye@126.com., Zhou T; College of Underwater Acoustic Engineering, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin, 150001, Heilongjiang, China. Electronic address: zhoutian@hrbeu.edu.cn. |
| Πηγή: | Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Jul; Vol. 199, pp. 108704. Date of Electronic Publication: 2026 Feb 10. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York : Pergamon Press, [c1988- |
| Ιατρικοί όροι (MeSH): | Data Mining*/methods , Machine Learning* , Attention*, Clustering Algorithms ; Autoencoder ; Humans ; Cluster Analysis ; Algorithms |
| Περίληψη: | Multi-view graph clustering, a fundamental task in data mining and machine learning, aims to partition nodes into disjoint groups by leveraging complementary information from multiple data sources. Although significant progress has been made, existing methods often struggle to effectively capture both the unique structural information within each view and the complementary relationships across different views. Moreover, the lack of mechanisms to enforce global semantic consistency frequently results in unstable consensus representations and degraded clustering quality. To address these issues, we propose a novel end-to-end method, Multi-view Graph Clustering via Dual attention fusion and Collaborative optimization (MGCDC). Specifically, each view is first encoded using a graph attention autoencoder to obtain view-specific node embeddings. These embeddings are then integrated via a view-level attention mechanism to generate a unified consensus representation. To guide the learning process, we introduce two collaborative optimization objectives. First, a cross-view cluster alignment loss is employed to jointly perform self-training learning on both the view-specific and consensus embeddings. Second, a semantic consistency enhancement loss is introduced to maximize mutual information between node embeddings and their corresponding cluster summaries. The entire model is optimized end-to-end by jointly learning node representations, integrating multi-view information, and refining cluster assignments. Extensive experiments on five benchmark datasets demonstrate that MGCDC achieves highly competitive performance compared to state-of-the-art methods. (Copyright © 2026 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Attention mechanism; Graph clustering; Multi-view clustering; Self-supervised learning |
| Entry Date(s): | Date Created: 20260215 Date Completed: 20260707 Latest Revision: 20260707 |
| Update Code: | 20260708 |
| DOI: | 10.1016/j.neunet.2026.108704 |
| PMID: | 41691831 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1879-2782 |
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| DOI: | 10.1016/j.neunet.2026.108704 |